Prediction of exosomal miRNA-based biomarkers for liquid biopsy

In this study, we investigated the properties of exosomal miRNAs to identify potential biomarkers for liquid biopsy. We collected 956 exosomal and 956 non-exosomal miRNA sequences from RNALocate and miRBase to develop predictive models. Our initial analysis reveals that specific nucleotides are preferred at certain positions in miRNAs associated with exosomes. We employed an alignment-based approach, artificial intelligence (AI) models, and ensemble methods for predicting exosomal miRNAs. For the alignment-based approach, we used a motif-based method with MERCI and a similarity-based method with BLAST, achieving high precision but low coverage of about 29%. The AI models, developed using machine learning, deep learning techniques, and large language models, achieved a maximum AUC of 0.707 and an MCC of 0.268 on an independent dataset. Finally, our ensemble method, combining alignment-based and AI-based models, reached a maximum AUC of 0.73 and an MCC of 0.352 on an independent dataset. We have developed a web server, EmiRPred, to assist the scientific community in predicting and designing exosomal miRNAs and identifying associated motifs (https://webs.iiitd.edu.in/raghava/emirpred/). Key pointsO_LIExosomal miRNAs have potential applications in liquid biopsy C_LIO_LIAn ensemble method has been developed to predict and design exosomal miRNA C_LIO_LIAn array of predictive models were built using alignment-based approaches and AI-based approaches (ML, DL, LLM) C_LIO_LIA variety of important features and motifs for exosomal miRNA have been identified C_LIO_LIA webserver, a python package, a github, and a standalone software have been created C_LI